带 dplyr 的条件线性拟合

Question

我正在尝试对分组数据进行线性拟合。

但是，我也想在整个拟合过程中添加一些条件，但是当我对某些条件进行子集化时却没有这样做。

set.seed(183)
library(dplyr)
V <- rep(seq(1,8),3)
value = c(c(sort(runif(5,0.001,1)),rep(0,3)),c(sort(runif(5,0.001,1)),rep(0,2),runif(1,0.001,1)),c(sort(runif(5,0.001,1)),rep(0,2),runif(1,0.001,1)))
group=rep(letters[1:3],each=8)

df <- data.frame(group,V,value)

#    > df
#   group V      value
#1      a 1 0.15087459
#2      a 2 0.35408406
#3      a 3 0.47339320
#4      a 4 0.67614665
#5      a 5 0.98273932
#6      a 6 0.00000000
#7      a 7 0.00000000
#8      a 8 0.00000000
#9      b 1 0.32821476
#10     b 2 0.35737009
#11     b 3 0.58821689
#12     b 4 0.81088053
#13     b 5 0.99122633
#14     b 6 0.00000000
#15     b 7 0.00000000
#16     b 8 0.03697432
#17     c 1 0.12940226
#18     c 2 0.41918905
#19     c 3 0.66020739
#20     c 4 0.84124155
#21     c 5 0.95052213
#22     c 6 0.00000000
#23     c 7 0.00000000
#24     c 8 0.15071444

我在各组内的条件是

1) if all last 3 value==0 fit only when V>=4&V<=5

2)if any last 2 value>0 fit only when V>=7.

这是我为执行此操作而编写的函数

   get_slope <- function(df){
  if (tail(df$value,3)==0)
    slp = coef(lm(value~V, data=subset(df,V>=4&V<=5)))[2]
    else 
    if (any(tail(df$value,3)>=0))
        slp = coef(lm(value ~ V, data=subset(df,V>=7)))[2]
    return(slp)
  }

df_slope <- df%>%
  group_by(group)%>%
  do(.,slope=get_slope(df))

Warning messages:
1: In if (tail(df$value, 3) == 0) slp = coef(lm(value ~ V, data = subset(df,  :
  the condition has length > 1 and only the first element will be used
2: In if (tail(df$value, 3) == 0) slp = coef(lm(value ~ V, data = subset(df,  :
  the condition has length > 1 and only the first element will be used
3: In if (tail(df$value, 3) == 0) slp = coef(lm(value ~ V, data = subset(df,  :
  the condition has length > 1 and only the first element will be used

最后我想得到每组的斜率值。

有没有简单的方法来做到这一点？

非常感谢！

Answer 1

条件tail(df$value,3)==0将给出3个T/F值。我在下面的函数中交换它，并使用 split 和 plyr::ldply 而不是 dplyr.

df1<-split(df, df$group)
get_slope <- function(df){
  if (any(!tail(df$value,3)==0)) coef(lm(value ~ V, data=subset(df,V>=7)))[2]
  else  coef(lm(value~V, data=subset(df,V>=4&V<=5)))[2]
}
library(plyr)
ldply(df1, get_slope)

  .id          V
1   a 0.06940913
2   b 0.20794964
3   c 0.84607397

请求的 dplyr 方法：

df_slope <- df %>% 
               group_by(group) %>% 
               do(.,slope=get_slope(.))

df_slope$slope <- df_slope$slope %>% unlist %>% as.numeric
df_slope

Source: local data frame [3 x 2]
Groups: <by row>

   group      slope
  (fctr)      (dbl)
1      a 0.06940913
2      b 0.20794964
3      c 0.84607397

Answer 2

我想到的是循环所有级别并应用函数。

1.set 接收所有结果的数组

    slp = c()

2.Loop组内所有级别，满足条件则执行动作。

 for( group in unique(df$group)){
     if(sum(tail(df$value[which(df$group==group)],3) == c(0,0,0))==3){
       slp = c(slp,coef(lm(value~V, data=subset(df,V>=4&V<=5)))[2])
      }else if(any(tail(df$value[which(df$group==group)],2)>=0)){
   slp = c(slp, coef(lm(value ~ V, data=subset(df,V>=7)))[2])
     }}

3.Print 结果

slp
         a          b          c 
0.06448301 0.55057826 0.55057826

我的解决方案可能不像预期的那样简单，但应该很容易理解，希望对您有所帮助。

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